November 9, 2019
Happy Valley Meat Company works with some pretty well known restaurants…
Momofuku Ko
Momofuku Ssam Bar
The Dutch
Dig Inn
Fette Sau
Frenchette
The weight of each cut of meat on one beef animal:
| cut | total_weight |
|---|---|
| Blended Burger | 379 |
| Ground Beef | 250 |
| Beef Trimmings | 250 |
| Beef Patties | 250 |
| Fat | 100 |
| Bones | 80 |
| Round | 64 |
| Chuck Roll | 60 |
| Bone-in Ribeye | 48 |
| Bone-in Strip | 36 |
##constants
water.rate <- 6.36 #(million gallons of water per 1 animal)
co2.emision <- 102.96 #(thousand lbs of co2 emissions per 1 animal)
land.use <- 77 #(acres of land per 1 animal)Source: Ranganathan, Janet, et al. “Shifting diets for a sustainable food future.” World Resources Institute (2016).
##load in the dplyr library
library(dplyr)
##entry weight for pound of meat
weight = 100
## calculations for enviornmental impact
carcass_calculator_data_100_pounds <- carcass_calculator_data %>%
mutate(number_cows = ceiling(weight/total_weight)) %>%
select(-total_weight)The number of beef animals required for 100 pounds of each cut:
| cut | number_cows |
|---|---|
| Blended Burger | 1 |
| Ground Beef | 1 |
| Beef Trimmings | 1 |
| Beef Patties | 1 |
| Fat | 1 |
| Bones | 2 |
| Round | 2 |
| Chuck Roll | 2 |
| Bone-in Ribeye | 3 |
| Bone-in Strip | 3 |
##calculate the enviornmental impact
carcass_calculator_data_100_pounds <- carcass_calculator_data_100_pounds %>%
mutate(water_rate = water.rate * number_cows) %>%
mutate(co2_emission = co2.emision * number_cows) %>%
mutate(land_use = land.use * number_cows) The enviornmental impact for 100 pounds of each of the following cuts:
| cut | number_cows | water_rate | co2_emission | land_use |
|---|---|---|---|---|
| Blended Burger | 1 | 6.36 | 102.96 | 77 |
| Ground Beef | 1 | 6.36 | 102.96 | 77 |
| Beef Trimmings | 1 | 6.36 | 102.96 | 77 |
| Beef Patties | 1 | 6.36 | 102.96 | 77 |
| Fat | 1 | 6.36 | 102.96 | 77 |
| Bones | 2 | 12.72 | 205.92 | 154 |
| Round | 2 | 12.72 | 205.92 | 154 |
| Chuck Roll | 2 | 12.72 | 205.92 | 154 |
| Bone-in Ribeye | 3 | 19.08 | 308.88 | 231 |
| Bone-in Strip | 3 | 19.08 | 308.88 | 231 |
## load in the required the packages
library(ggplot2)
library(wesanderson)
##create a color palette
values=wes_palette(n=4, name="Darjeeling1")
##reorder the data in order of number of cows
plot.data <- carcass_calculator_data_100_pounds %>%
mutate(cut = reorder(cut, number_cows))
##plot th data in a barplot
plot.out <- ggplot(plot.data,
aes(x = cut,
y = number_cows)) +
geom_bar(stat="identity", fill = values[4]) +
coord_flip() +
xlab('') +
ylab('Animals') +
theme(legend.position="none",
axis.title=element_text(size=12),
plot.title=element_text(size = 16)) +
ggtitle(paste0('Animals for ', weight, ' Pounds of Meat by Cut'))The %+% function allows you to replace the data frame that you are using with ggplot!
## make a vector of a subset of the cuts
cut.list <- c('Tongue',
'Ground Beef',
'Tenderloin',
'Bone-in Ribeye',
'Skirt')
## plot the data for the subset of the cuts
plot.out %+% filter(plot.data, cut %in% cut.list) +
theme(axis.text=element_text(size=14))library(plotly)
ggplotly(plot.out, tooltip = c("x", "y"))| Lat | Lng | Distance..miles. | Elevation..feet. |
|---|---|---|---|
| 42.13400 | -74.10404 | 0.0000000 | 2054 |
| 42.13383 | -74.10510 | 0.0556631 | 2073 |
| 42.13324 | -74.10629 | 0.1288833 | 2060 |
| 42.13232 | -74.10648 | 0.1928586 | 2067 |
| 42.13137 | -74.10583 | 0.2669656 | 2077 |
| 42.13041 | -74.10542 | 0.3368528 | 2100 |
x <- list(
title = "Distance (in miles)"
)
y <- list(
title = "Elevation (in feet)"
)
plot_ly(data = devils.path,
x = ~Distance..miles.,
y = ~Elevation..feet.,
type = 'scatter',
mode = 'lines',
color = I('purple'),
hoverinfo = 'text',
text = paste(
round(devils.path$Distance..miles., 1),
"miles, ",
round(devils.path$Elevation..feet., 0),
"feet elevation"),
fill = 'tozeroy') %>%
layout(xaxis = x,
yaxis = y,
title = "The Devil's Path",
shapes=list(type='line',
y0= 3500,
y1= 3500,
x0=min(devils.path$Distance..miles.),
x1=max(devils.path$Distance..miles.),
line=list(dash='dot', width=1)))